{"id":16343004,"url":"https://github.com/sukhmancs/recommender_system","last_synced_at":"2025-11-05T14:30:35.502Z","repository":{"id":225078722,"uuid":"764998955","full_name":"sukhmancs/recommender_system","owner":"sukhmancs","description":"This repository contains an implementation of a text document recommender system using Python. The system recommends similar documents based on vector representations and similarity calculations.","archived":false,"fork":false,"pushed_at":"2024-02-29T08:12:51.000Z","size":59890,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-12-27T13:46:03.277Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/sukhmancs.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-02-29T05:00:31.000Z","updated_at":"2024-02-29T05:15:00.000Z","dependencies_parsed_at":"2024-12-27T13:44:30.709Z","dependency_job_id":"9755d799-caca-4afc-b835-5b5c9407278c","html_url":"https://github.com/sukhmancs/recommender_system","commit_stats":{"total_commits":9,"total_committers":2,"mean_commits":4.5,"dds":0.4444444444444444,"last_synced_commit":"60c2225727977ada4c32db6c0aa624b1f6444a92"},"previous_names":["sukhmancs/recommender_system"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sukhmancs%2Frecommender_system","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sukhmancs%2Frecommender_system/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sukhmancs%2Frecommender_system/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sukhmancs%2Frecommender_system/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sukhmancs","download_url":"https://codeload.github.com/sukhmancs/recommender_system/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":239461795,"owners_count":19642640,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-10-11T00:05:51.746Z","updated_at":"2025-02-18T11:27:57.449Z","avatar_url":"https://github.com/sukhmancs.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cdiv id=\"header\" align=\"center\"\u003e\n  \u003ch1\u003e\n    🚀 Recommender System\n    \u003cimg src=\"https://media.giphy.com/media/v1.Y2lkPTc5MGI3NjExZ2g0M2owejB2MHAxbnluN21sZnp3eG1taGNyYXc5dTc0OHA1Y2FqcyZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9cw/lOgu1OnjYF2GHBfRU4/giphy.gif\" width=\"40px\"/\u003e\n  \u003c/h1\u003e\n\u003c/div\u003e\n\nThis repository contains an implementation of a text document recommender system using Python. The system recommends similar documents based on vector representations and similarity calculations.\nOverview\n\nRecommender systems such as this are a core application of statistical AI. At the heart of recommender systems is a similarity calculation. In this implementation, we use vector representations of documents and a document similarity calculation to recommend relevant articles to users.\n\n# Features\n\n- Load text documents from various datasets (e.g., BBC news articles, scientific abstracts, Wikipedia articles).\n- Preprocess text data to remove stopwords and perform lemmatization.\n- Vectorize documents using TF-IDF vectorization.\n- Calculate document similarity using cosine similarity.\n- Generate recommendations based on the most similar documents to a selected document, while also including some less similar documents to provide diversity.\n- Avoid recommending the same document or documents with the same title as the selected document.\n\n# Usage\n\nClone the repository:\n```bash\n\ngit clone https://github.com/your_username/text-document-recommender.git\n```\n\nRun the recommender system:\n```bash\npython recommender.py\n```\n\n# Dataset\nThe datasets used in this project include:\n\n- BBC news articles\n- Scientific abstracts\n- Wikipedia articles\n\nThese datasets have been adapted for this task.\n\n# License\n\nThis project is licensed under the MIT License. See the [LICENSE file](./LICENSE) for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsukhmancs%2Frecommender_system","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsukhmancs%2Frecommender_system","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsukhmancs%2Frecommender_system/lists"}